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Clustering and risk factor analysis of pulmonary tuberculosis in a district in Ethiopia: a population-based cohort
Abiot Bezabeh Banti1,2, Daniel Gemechu Datiko3, Brita Askeland Winje4
1Global Public Health and Primary Care, University of Bergen, Bergen, Norway Abiot.Banti@uib.no.
Objective:
A 'cluster' is an area with a higher occurrence of tuberculosis (TB) than would be expected in an average random distribution of that area. Tuberculosis clustering is commonly reported in Ethiopia, but most studies rely on registered data, which may miss patients who do not visit health facilities or those who attend but are not identified as having TB. This makes the detection of actual clusters challenging. This study analysed the clustering of pulmonary TB and associated risk factors using symptom-based population screening in Dale, Ethiopia.
Design:
A prospective population-based cohort study.
Setting:
All households in 383 enumeration areas were visited three times over a 1-year period, at 4-month intervals.
Participants:
Individuals with pulmonary TB aged ≥15 years with demographic, socioeconomic, clinical and geographical data residing in 383 enumeration areas (ie, the lowest unit/village in the kebele, each with approximately 600 residents).
Outcome Measures:
Pulmonary TB (ie, bacteriologically confirmed by sputum microscopy, GeneXpert or culture plus clinically diagnosed pulmonary TB) and pulmonary TB clustering.
Results:
We identified pulmonary TB clustering in 45 out of the 383 enumeration areas. During the first round of screening, 39 enumeration areas showed pulmonary TB clustering, compared with only 3 enumeration areas in the second and third rounds. Our multilevel analysis found that enumeration areas with clusters were located farther from the health centres than other enumeration areas. No other determinants examined were associated with clustering.
Conclusions:
The distribution of pulmonary TB was clustered in enumeration areas distant from the health centres. Routine systematic community screening may be costly, but using existing health infrastructure with health extension workers through targeted screening, they can identify and refer persons with TB symptoms more quickly for diagnosis and treatment, thereby decreasing the duration of disease transmission and contributing to the reduction of TB burden.
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